Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "15" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 17 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 17 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459994 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.065223 | 14.437431 | 10.130870 | 10.958963 | 7.730282 | 9.536814 | 0.023785 | 0.254260 | 0.0264 | 0.0256 | 0.0014 | nan | nan |
| 2459991 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.939311 | 16.196488 | 9.454638 | 10.218791 | 9.092976 | 10.721991 | -0.229557 | -0.088883 | 0.0279 | 0.0264 | 0.0017 | nan | nan |
| 2459990 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.501592 | 13.340381 | 9.253698 | 9.916832 | 8.992431 | 11.003475 | -0.444727 | -0.402359 | 0.0279 | 0.0265 | 0.0016 | nan | nan |
| 2459989 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.287405 | 13.530400 | 8.232850 | 9.075843 | 7.939283 | 9.226087 | -0.425093 | -0.313770 | 0.0278 | 0.0264 | 0.0016 | nan | nan |
| 2459988 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.490317 | 16.441401 | 10.078616 | 10.735644 | 10.730129 | 13.196268 | -0.207651 | -0.003778 | 0.0264 | 0.0255 | 0.0013 | nan | nan |
| 2459987 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.113280 | 13.242327 | 9.260927 | 10.073327 | 6.337237 | 7.954481 | 0.538220 | 1.155436 | 0.0272 | 0.0264 | 0.0015 | nan | nan |
| 2459986 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.855833 | 16.865851 | 10.704816 | 11.434037 | 9.327376 | 11.245597 | 5.236636 | 9.192620 | 0.0270 | 0.0256 | 0.0017 | nan | nan |
| 2459985 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.811743 | 15.283478 | 9.918253 | 10.652425 | 7.186243 | 8.601040 | 0.502871 | 0.826389 | 0.0265 | 0.0256 | 0.0013 | nan | nan |
| 2459984 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.027892 | 14.130848 | 9.755739 | 10.499861 | 9.381529 | 12.069178 | 1.621726 | 1.997947 | 0.0272 | 0.0268 | 0.0014 | nan | nan |
| 2459983 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.809157 | 13.839953 | 9.331938 | 9.920330 | 9.200454 | 11.120916 | 2.436796 | 5.496889 | 0.0277 | 0.0264 | 0.0016 | nan | nan |
| 2459982 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 9.278531 | 11.684740 | 8.364934 | 8.927213 | 4.488833 | 5.270904 | 2.359849 | 3.110703 | 0.0269 | 0.0255 | 0.0017 | nan | nan |
| 2459981 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.244436 | 13.227793 | 10.499749 | 11.114772 | 10.388895 | 12.344894 | -0.107858 | 0.179958 | 0.0264 | 0.0257 | 0.0014 | nan | nan |
| 2459980 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 9.929467 | 12.320727 | 8.927192 | 9.642409 | 8.935138 | 10.751246 | 4.978246 | 5.071248 | 0.0278 | 0.0264 | 0.0016 | nan | nan |
| 2459979 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.300930 | 12.811025 | 8.252923 | 9.008725 | 8.857904 | 10.067708 | -0.514027 | -0.305832 | 0.0287 | 0.0264 | 0.0014 | nan | nan |
| 2459978 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.554718 | 13.535443 | 9.491492 | 10.231791 | 9.298421 | 10.955730 | -0.380971 | 0.004456 | 0.0267 | 0.0253 | 0.0016 | nan | nan |
| 2459977 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.721067 | 13.762411 | 8.825752 | 9.577611 | 9.137568 | 11.246997 | -0.510328 | -0.076953 | 0.0277 | 0.0264 | 0.0016 | nan | nan |
| 2459976 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.783319 | 13.759912 | 9.811465 | 10.503521 | 9.363085 | 10.834391 | 0.413417 | 0.523022 | 0.0265 | 0.0256 | 0.0014 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 15 | N01 | digital_ok | nn Shape | 14.437431 | 11.065223 | 14.437431 | 10.130870 | 10.958963 | 7.730282 | 9.536814 | 0.023785 | 0.254260 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 15 | N01 | digital_ok | nn Shape | 16.196488 | 12.939311 | 16.196488 | 9.454638 | 10.218791 | 9.092976 | 10.721991 | -0.229557 | -0.088883 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 15 | N01 | digital_ok | nn Shape | 13.340381 | 13.340381 | 10.501592 | 9.916832 | 9.253698 | 11.003475 | 8.992431 | -0.402359 | -0.444727 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 15 | N01 | digital_ok | nn Shape | 13.530400 | 13.530400 | 10.287405 | 9.075843 | 8.232850 | 9.226087 | 7.939283 | -0.313770 | -0.425093 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 15 | N01 | digital_ok | nn Shape | 16.441401 | 16.441401 | 12.490317 | 10.735644 | 10.078616 | 13.196268 | 10.730129 | -0.003778 | -0.207651 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 15 | N01 | digital_ok | nn Shape | 13.242327 | 10.113280 | 13.242327 | 9.260927 | 10.073327 | 6.337237 | 7.954481 | 0.538220 | 1.155436 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 15 | N01 | digital_ok | nn Shape | 16.865851 | 16.865851 | 12.855833 | 11.434037 | 10.704816 | 11.245597 | 9.327376 | 9.192620 | 5.236636 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 15 | N01 | digital_ok | nn Shape | 15.283478 | 15.283478 | 11.811743 | 10.652425 | 9.918253 | 8.601040 | 7.186243 | 0.826389 | 0.502871 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 15 | N01 | digital_ok | nn Shape | 14.130848 | 11.027892 | 14.130848 | 9.755739 | 10.499861 | 9.381529 | 12.069178 | 1.621726 | 1.997947 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 15 | N01 | digital_ok | nn Shape | 13.839953 | 10.809157 | 13.839953 | 9.331938 | 9.920330 | 9.200454 | 11.120916 | 2.436796 | 5.496889 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 15 | N01 | digital_ok | nn Shape | 11.684740 | 9.278531 | 11.684740 | 8.364934 | 8.927213 | 4.488833 | 5.270904 | 2.359849 | 3.110703 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 15 | N01 | digital_ok | nn Shape | 13.227793 | 13.227793 | 10.244436 | 11.114772 | 10.499749 | 12.344894 | 10.388895 | 0.179958 | -0.107858 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 15 | N01 | digital_ok | nn Shape | 12.320727 | 12.320727 | 9.929467 | 9.642409 | 8.927192 | 10.751246 | 8.935138 | 5.071248 | 4.978246 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 15 | N01 | digital_ok | nn Shape | 12.811025 | 10.300930 | 12.811025 | 8.252923 | 9.008725 | 8.857904 | 10.067708 | -0.514027 | -0.305832 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 15 | N01 | digital_ok | nn Shape | 13.535443 | 13.535443 | 10.554718 | 10.231791 | 9.491492 | 10.955730 | 9.298421 | 0.004456 | -0.380971 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 15 | N01 | digital_ok | nn Shape | 13.762411 | 10.721067 | 13.762411 | 8.825752 | 9.577611 | 9.137568 | 11.246997 | -0.510328 | -0.076953 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 15 | N01 | digital_ok | nn Shape | 13.759912 | 13.759912 | 10.783319 | 10.503521 | 9.811465 | 10.834391 | 9.363085 | 0.523022 | 0.413417 |